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Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications.

ABSTRACT: Dropout events in single-cell RNA sequencing (scRNA-seq) cause many transcripts to go undetected and induce an excess of zero read counts, leading to power issues in differential expression (DE) analysis. This has triggered the development of bespoke scRNA-seq DE methods to cope with zero inflation. Recent evaluations, however, have shown that dedicated scRNA-seq tools provide no advantage compared to traditional bulk RNA-seq tools. We introduce a weighting strategy, based on a zero-inflated negative binomial model, that identifies excess zero counts and generates gene- and cell-specific weights to unlock bulk RNA-seq DE pipelines for zero-inflated data, boosting performance for scRNA-seq.

SUBMITTER: Van den Berge K

PROVIDER: S-EPMC6251479 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications.

Van den Berge Koen K Perraudeau Fanny F Soneson Charlotte C Love Michael I MI Risso Davide D Vert Jean-Philippe JP Robinson Mark D MD Dudoit Sandrine S Clement Lieven L

Genome biology 20180226 1

Dropout events in single-cell RNA sequencing (scRNA-seq) cause many transcripts to go undetected and induce an excess of zero read counts, leading to power issues in differential expression (DE) analysis. This has triggered the development of bespoke scRNA-seq DE methods to cope with zero inflation. Recent evaluations, however, have shown that dedicated scRNA-seq tools provide no advantage compared to traditional bulk RNA-seq tools. We introduce a weighting strategy, based on a zero-inflated neg ...[more]

PMID: 29478411

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